• Title/Summary/Keyword: Radar data visualization

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Evaluation of Corrosion Protection Efficiency and Analysis of Damage Detectability in Buried Pipes of a Nuclear Power Plant with 3D FEM (3D FEM 모델링을 이용한 원전 매설배관의 방식성능 평가 및 결함탐지능 분석)

  • Chang, Hyun Young;Park, Heung Bae;Kim, Ki Tae;Kim, Young Sik;Jang, Yoon Young
    • Transactions of the Korean Society of Pressure Vessels and Piping
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    • v.11 no.2
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    • pp.61-67
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    • 2015
  • 3D FEM modeling based on 3D CAD data has been performed to evaluate the efficiency of CP system in a real operating nuclear power plant. The results of it successfully produced sophisticated profiles of electrolytic potential and current distributions in the soil of an interested area. This technology is expected to be a breakthrough for detection technology of damages on buried pipes when it comes into combining with a brand of area potential earth current (APEC) and ground penetrated radar (GPR) technologies. 2D current distribution and 2D current vectors on the earth surface from the APEC survey will be used as boundary conditions with exact 3D geometry data resulting in visualization of locations and extents of corrosion damages on the buried pipes in nuclear power plants.

Advanced signal processing for enhanced damage detection with piezoelectric wafer active sensors

  • Yu, Lingyu;Giurgiutiu, Victor
    • Smart Structures and Systems
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    • v.1 no.2
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    • pp.185-215
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    • 2005
  • Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

A Methodology of AI Learning Model Construction for Intelligent Coastal Surveillance (해안 경계 지능화를 위한 AI학습 모델 구축 방안)

  • Han, Changhee;Kim, Jong-Hwan;Cha, Jinho;Lee, Jongkwan;Jung, Yunyoung;Park, Jinseon;Kim, Youngtaek;Kim, Youngchan;Ha, Jeeseung;Lee, Kanguk;Kim, Yoonsung;Bang, Sungwan
    • Journal of Internet Computing and Services
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    • v.23 no.1
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    • pp.77-86
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    • 2022
  • The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

Convective Cloud RGB Product and Its Application to Tropical Cyclone Analysis Using Geostationary Satellite Observation

  • Kim, Yuha;Hong, Sungwook
    • Journal of the Korean earth science society
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    • v.40 no.4
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    • pp.406-413
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    • 2019
  • Red-Green-Blue (RGB) imagery techniques are useful for both forecasters and public users because they are intuitively understood, have advantageous visualization, and do not lose observational information. This study presents a novel RGB convective cloud product and its application to tropical cyclone analysis using Communication, Oceanography, and Meteorology (COMS) satellite observations. The RGB convective cloud product was developed using the brightness temperature differences between WV ($6.75{\mu}m$) and IR1 ($10.8{\mu}m$), and IR2 ($12.0{\mu}m$) and IR1 ($10.8{\mu}m$) as well as the brightness temperature in the IR1 bands of the COMS, with the threshold values estimated from the Korea Meteorological Administration (KMA) radar observations and the EUMETSAT RGB recipe. To verify the accuracy of the convective cloud RGB product, the product was applied to the center positions analysis of two typhoons in 2013. Thus, the convective cloud RGB product threshold values were estimated for WV-IR1 (-20 K to 15 K), IR1 (210 K to 300 K), and IR1-IR2 (-4 K to 2 K). The product application in typhoon analysis shows relatively low bias and root mean square errors (RMSE)s of 23 and 28 km for DANAS in 2013, and 17 and 22 km for FRANCISCO in 2013, as compared to the best tracks data from the Regional Specialized Meteorological Center (RSMC) in Tokyo. Consequently, our proposed RGB convective cloud product has the advantages of high accuracy and excellent visualization for a variety of meteorological applications.

Delamination and concrete quality assessment of concrete bridge decks using a fully autonomous RABIT platform

  • Gucunski, Nenad;Kee, Seong-Hoon;La, Hung;Basily, Basily;Maher, Ali
    • Structural Monitoring and Maintenance
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    • v.2 no.1
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    • pp.19-34
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    • 2015
  • One of the main causes of a limited use of nondestructive evaluation (NDE) technologies in bridge deck assessment is the speed of data collection and analysis. The paper describes development and implementation of the RABIT (Robotics Assisted Bridge Inspection Tool) for data collection using multiple NDE technologies. The system is designed to characterize three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. It implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW) method. The technologies are used in a complementary way to enhance the interpretation. In addition, the system utilizes advanced vision to complement traditional visual inspection. Finally, the RABIT collects data at a significantly higher speed than it is done using traditional NDE equipment. The robotic system is complemented by an advanced data interpretation. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. This paper concentrates on the validation and field implementation of two NDE technologies. The first one is IE used in the delamination detection and characterization, while the second one is the USW method used in the assessment of concrete quality. The validation of performance of the two methods was conducted on a 9 m long and 3.6 m wide fabricated bridge structure with numerous artificial defects embedded in the deck.

A Study of Disposition of Archaeological Remains in Wolseong Fortress of Gyeongju : Using Ground Penetration Radar(GPR) (GPR탐사를 통해 본 경주 월성의 유적 분포 현황 연구)

  • Oh, Hyun Dok;Shin, Jong Woo
    • Korean Journal of Heritage: History & Science
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    • v.43 no.3
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    • pp.306-333
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    • 2010
  • Previous studies on Wolseong fortress have focused on capital system of Silla Dynasty and on the recreation of Wolseong fortress due to the excavations in and around Wolseong moat. Since the report on the Geographical Survey of Wolseong fortress was published and GPR survey in Wolseong fortress was executed as a trial test in 2004, the academic interest in the site has now expanded to the inside of the fortress. From such context, the preliminary research on the fortress including geophysical survey had been commenced. GPR survey had been conducted for a year from March, 2007. The principal purpose of the recent 3D GPR survey was to provide visualization of subsurface images of the entire Wolseong fortress area. In order to obtain 3D GPR data, dense profile lines were laid in grid-form. The total area surveyed was $112,535m^2$. Depth slice was applied to analyse each level to examine how the layers of the remains had changed and overlapped over time. In addition, slice overlay analysis methodology was used to gather reflects of each depth on a single map. Isolated surface visualization, which is one of 3D analysis methods, was also employed to gain more in-depth understanding and more accurate interpretations of the remain The GPR survey has confirmed that there are building sites whose archaeological features can be classified into 14 different groups. Three interesting areas with huge public building arrangement have been found in Zone 2 in the far west, Zone 9 in the middle, and Zone 14 in the far east. It is recognized that such areas must had been used for important public functions. This research has displayed that 3D GPR survey can be effective for a vast area of archaeological remains and that slice overlay images can provide clearer image with high contrast for objects and remains buried the site.